To facilitate the early gastric cancer diagnosis, an assay based on assessing large-scale methylation and fragmentation profiles of the plasma cell free (cfDNA) will be developed and validated.
Cancer-related features in cell-free DNA (cfDNA) fragments have gradually been identified and play essential roles for non-invasive early cancer detection. Integrated analysis of several cfDNA features have proven to achieve enhanced detection sensitivity as compared to single feature. This study aims to develop and validate a novel blood-based whole methylome sequencing followed with a multi-dimensional model to analyze several features of cfDNA for GC early detection. Specifically, blood samples will be prospectively collected before gastroscopy. Cases and controls will be randomly divided into a training and a testing dataset at a ratio of 2:1. Plasma cfDNA will be isolated and extracted, followed with a bisulfite-free low-depth whole methylome sequencing. A multi-dimensional model named THorough Epigenetic Marker Integration Solution (THEMIS) including methylation, fragmentation, and chromosomal copy number alternation will be constructed in the training dataset. The performance of the model in differentiating cancer patients from non-cancer controls will then be evaluated in the testing dataset.
Study Type
OBSERVATIONAL
Enrollment
360
The assay for gastric cancer early detection will be built based on low-depth methylone sequencing followed with a multi-dimensional model construction with analysing several features such as methylation, fragmentation, and chromosomal copy number alternation.
The First Affiliated Hospital of Air Force Military Medical University (Xijing Hospital)
Xi'an, Shaanxi, China
RECRUITINGThe performance of each single feature and the ensemble model with integrated features for early GC detection
The efficacy of each single feature-based model and the ensemble model comparing with pathologic diagnostic results, the gold standard, and gastroscope diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
Time frame: 18 months
The performance of each single feature and the ensemble model with integrated features for early GC detection in each clinical stage
The efficacy of each single feature-based model and the ensemble model comparing with pathologic diagnostic results, the gold standard, and gastroscope diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
Time frame: 18 months
The performance of the ensemble model in combination of possible GC related biomarkers such as PG, G17, and/or Hp levels for early GC detection
The efficacy of the integrated model comparing with pathologic diagnostic results, the gold standard, and gastroscope diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
Time frame: 18 months
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